The lack of fungibility in Bitcoin has forced its userbase to seek out tools that can heighten their anonymity. Third-party Bitcoin mixers use obfuscation techniques to protect participants from blockchain transaction analysis. In recent years, various centralized and decentralized Bitcoin mixing methods were proposed in academic literature (e.g., CoinJoin, CoinShuffle). Although these methods strive to create a threat-free environment for users to preserve their anonymity, public Bitcoin mixers continue to be associated with theft and poor implementation. This paper explores the public Bitcoin mixer ecosystem to identify if today’s mixing services have adopted academia’s proposed solutions. We perform real-world interactions with publicly available mixers to analyze both implementation and resistance to common threats in the mixing landscape. We present data from 21 publicly available mixing services on the deep web and clearnet. Our results highlight a clear gap between public and proposed Bitcoin mixers in both implementation and security. We find that the majority of key security features proposed by academia are not deployed in any public Bitcoin mixers that are trusted most by Bitcoin users. Today’s mixing services focus on presenting users with a false sense of control to gain their trust rather than employing secure mixing techniques.